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09/05/2022
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
The quantification of uncertainty is important for the adoption of machi...
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02/18/2022
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
We explore the utility of information contained within a dropout based B...
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08/09/2021
Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints
The detection of malware is a critical task for the protection of comput...
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09/01/2020
Practical Cross-modal Manifold Alignment for Grounded Language
We propose a cross-modality manifold alignment procedure that leverages ...
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07/01/2020
An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
Non-pharmaceutical interventions (NPIs) have been crucial in curbing COV...
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11/12/2019
Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturb...
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11/06/2019
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
We introduce the use of a Gated Recurrent Unit (GRU) for influenza predi...
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10/10/2019
Would a File by Any Other Name Seem as Malicious?
Successful malware attacks on information technology systems can cause m...
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08/24/2019
Heterogeneous Relational Kernel Learning
Recent work has developed Bayesian methods for the automatic statistical...
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07/17/2019
Connecting Lyapunov Control Theory to Adversarial Attacks
Significant work is being done to develop the math and tools necessary t...
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12/07/2018
